Some background:
Hi all! I have a project which involves cloud imaging. I take pictures of the sky using a camera mounted on a rotating platform. I then need to compute the amount of cloud present based on some color threshold. I am able to this individually for each picture. To completely achieve my goal, I need to do the computation on the whole image of the sky. So my problem lies with stitching several images (about 44-56 images). I've tried using the stitch function on all and some subsets of image set but it returns an incomplete image (some images were not stitched). This could be because of a lack of overlap of something, I dunno. Also the output image has been distorted weirdly (I am actually expecting the output to be something similar to a picture taken by a fish-eye lense).
The actual problem:
So now I'm trying to figure out the opencv stitching pipeline. Here is a link:
http://docs.opencv.org/modules/stitching/doc/introduction.html
Based on what I have researched I think this is what I want to do. I want to map all the images to a circular shape, mainly because of the way how my camera rotates, or something else that has uses a fairly simple coordinate transformation. So I think I need get some sort of fixed coordinate transform thing for the images. Is this what they call the homography? If so, does anyone have any idea how I can go about my problem? After this, I believe I need to get a mask for blending the images. Will I need to get a fixed mask like the one I want for my homography?
Am I going through a possible path? I have some background in programming but almost none in image processing. I'm basically lost. T.T
"So I think I need get some sort of fixed coordinate transform thing for the images. Is this what they call the homography?"
Yes, the homography matrix is the transformation matrix between an original image and the ideal result. It warps an image in perspective so it can fit in stitching to the other image.
"If so, does anyone have any idea how I can go about my problem?"
Not with the limited information you provided. It would ease the problem a lot if you know the order of pictures (which borders which.. row, column position)
If you have no experience in image processing, I would recommend you use a tutorial covering stitching using more basic functions in detail. There is some important work behind the scenes, and it's not THAT harder to actually do it yourself.
Start with this example. It stitches two pictures.
http://ramsrigoutham.com/2012/11/22/panorama-image-stitching-in-opencv/
Related
I am trying to do image alignment like posted on adrian blog like this image or in this link.
I want to do image alignment on this kind of image. The problem is I want to automatically detect the 4 point edges which are hard to detect in this kind of images with contour detection like in the tutorial.
Now I can do alignment just fine with manually input edge coordinates. Some of my friends suggest me to detect the edges with dlib landmark detection, but as far as I can see it mostly uses shape in which dlib automatically marking the landmark.
Do I miss something here? Or is there any tutorial or even basic guide about how to do that?
Maybe you can try to detect edges on a Gaussian pyramid. You can find an explanation here https://en.wikipedia.org/wiki/Pyramid_(image_processing). The basic idea is that by filtering with Gaussian filters of increasing size, the small objects are blurred. Thus at some scale, we get only edges of the showcase (maybe need further processing).
Here is the tutorial of opencv on image pyramid: https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_pyramids/py_pyramids.html.
I think wavelet pyramid (do wavelet transform several times) may work for your problem, since wavelet can reduce the details in image.
currently i am having much difficulty thinking of a good method of removing the gradient from a image i received.
The image is a picture taken by a microscope camera that has a light glare in the middle. The image has a pattern that goes throughout the image. However i am supposed to remove the light glare on the image created by the camera light.
Unfortunately due to the nature of the camera it is not possible to take a picture on black background with the light to find the gradient distribution. Nor do i have a comparison image that is without the gradient. (note- the location of the light glare will always be consistant when the picture is taken)
In easier terms its like having a photo with a flash in it but i want to get rid of the flash. The only problem is i have no way to obtaining the image without flash to compare to or even obtaining a black image with just the flash on it.
My current thought is conduct edge detection and obtain samples in specific locations away from the edges (due to color difference) and use that to gauge the distribution of gradient since those areas are supposed to have relatively identical colors. However i was wondering if there was a easier and better way to do this.
If needed i will post a example of the image later.
At the moment i have a preferrence of solving this in c++ using opencv if that makes it easier.
thanks in advance for any possible ideas for this problem. If there is another link, tutorial, or post that may solve my problem i would greatly appreciate the post.
as you can tell there is a light thats being shinned on the img as you can tell from the white spot. and the top is lighter than the bottome due to the light the color inside the oval is actually different when the picture is taken in color. However the color between the box and the oval should be consistant. My original idea was to perhaps sample only those areas some how and build a profile that i can utilize to remove the light but i am unsure how effective that would be or if there is a better way
EDIT :
Well i tried out Roger's suggestion and the results were suprisngly good. Using 110 kernel gaussian blurr to find illumination and conducting CLAHE on top of that. (both done in opencv)
However my colleage told me that the image doesn't look perfectly uniform and pointed out that around the area where the light used to be is slightly brighter. He suggested trying a selective gaussian blur where the areas above certain threshold pixel values are not blurred while the rest of the image is blurred.
Does anyone have opinions regarding this and perhaps a link, tutorial, or an example of something like this being done? Most of the things i find tend to be selective blur for programs like photoshop and gimp
EDIT2 :
it is difficult to tell with just eyes but i believe i have achieved relatively close uniformization by using a simple plane fitting algorithm.((-A * x - B * y) / C) (x,y,z) where z is the pixel value. I think that this can be improved by utilizing perhaps a sine fitting function? i am unsure. But I am relatively happy with the results. Many thanks to Roger for the great ideas.
I believe using a bunch of pictures and getting the avg would've been another good method (suggested by roger) but Unofruntely i was not able to implement this since i was not supplied with various pictures and the machine is under modification so i was unable to use it.
I have done some work in this area previously and found that a large Gaussian blur kernel can produce a reasonable approximation to the background illumination. I will try to get something working on your example image but, in the meantime, here is an example of your image after Gaussian blur with radius 50 pixels, which may help you decide if it's worth progressing.
UPDATE
Just playing with this image, you can actually get a reasonable improvement using adaptive histogram equalisation (I used CLAHE) - see comparison below - any use?
I will update this answer with more details as I progress.
I would like to point you to this paper: http://www.cs.berkeley.edu/~ravir/dirtylens.pdf, but, in my opinion, without any sort of calibration/comparison image taken apriori, it is difficult to mine out the ground truth from the flared image.
However, if you are trying to just present the image minus the lens flare, disregarding the actual scientific data behind the flared part, then you switch into the domain of image inpainting. Criminsi's algorithm, as described in this paper: http://research.microsoft.com/pubs/67276/criminisi_tip2004.pdf and explained/simplified in these two links: http://cs.brown.edu/courses/csci1950-g/results/final/eboswort/ http://www.cc.gatech.edu/~sooraj/inpainting/, will do a very good job in restoring texture information to the flared up regions. (If you'd really like to pursue this approach, do mention that. More comprehensive help can be provided for this).
However, given the fact that we're dealing with microscopic data, I doubt if you'd like to lose the scientific data contained in a particular region of an image. In that case, I really think you need to find a workaround to determine the flare model of the flash/light source w.r.t the lens you're using.
I hope someone else can shed more light on this.
I want to blend multiple photo shots of same scene but only one object is in different position on every shot. I want to know what kind of algorithm would give desired results. Here is an example
Well, what you are looking for is called Image Fusion. There are many methods that do this, but it is still a fairly active research idea. Based on the images you have, you should select the one that performs the best. Because your images will have imperfections and lighting, shadowing differences this is way beyond than a simple cut and paste.
Here is a little more information and some algorithm explanations: Image Fusion by Image Blending.
I'll first tell you the problem and then I'll tell you my solution.
Problem: I have a blank white PNG image approximately 900x900 pixels. I want to copy circles 30x30 pixels in size, which are essentially circles with a different colour. There are 8 different circles, and placed on the image depending on data values which I've created elsewhere.
Solution: I've used ImageMagicK, it's suppose to be good for general purpose image editing etc. I created a blank image
Image.outimage("900x900","white");
I upload all other small 30x30 pixel images with 'read' function.
I upload the data and extract vales.
I place the small 'circle' images on the blank one using the composite command.
outimage.composite("some file.png",pixelx,pixely,InCompositeOp);
This all works fine and the images come up the way I want them too.
However its painfully SLOW. It takes 20 seconds to do one image, and I have 1000 of them. Surely there must be a better way to do this. I've seen other researchers simulate images way more complex and way faster. It's quite possible I took the wrong approach. Maybe I sould be 'drawing' circles instead of 'pasting' them or something. I'm quite baffled. Any input is appreciated.
I suspect that you just need some library that is capable of drawing circles on bitmap and saving that bitmap as png.
For example my Graphin library: http://code.google.com/p/graphin/
Or some such. With Graphin you can also draw one PNG on surface of another as in your case.
You did not give any information about the platform you are using (only "C++"), so if you are looking for a platform independent solution, the CImg library might be worth a try.
http://cimg.sourceforge.net/
By the way, did you try drawing the circles using the ImageMagick C++ API Magick++ instead of "composing" them? I cannot believe that it is that slow.
I am working on a project to stitch together around 400 high resolution aerial images around 36000x2600 to create a map. I am currently using OpenCV and so far I have obtained the match points between the images. Now I am at a lost in figuring out how to get the matrix transformation of the images so I can begin the stitching process. I have absolutely no background in working with images nor graphics so this is a first time for me. Can I get some advice on how I would approach this?
The images that I received also came with a data sheet showing longitude, latitude, airplane wing angle, altitude, etc. of each image. I am unsure how accurate these data are, but I am wondering if I can use these information to perform the proper matrix transformation that I need.
Thanks
Do you want to understand the math behind the process or just have an superficial idea of whats going on and just use it?
The regular term for "image snitching" is image alignment. Feed google with it and you'll find tons of sources.
For example, here.
Best regards,
zhengtonic
In recent opencv 2.3 release...they implemented a whole process of image stitching. Maybe it is worth looking at.